Name
Abstract | ||
|---|---|---|
The competitive scene in online video games is becoming more and more prominent and player satisfaction is of key importance when it comes to a good user experience and a successful game. As such it is important to have efficient skill rating and matchmaking systems in order to provide a proper match experience. We propose a mathematical framework for the analysis of matchmaking systems. The mathematical model addresses the estimated skill or rating, calculation of winning probabilities based on the estimated skill, and the updating of the estimated skill upon completion of a game. We will briefly apply the framework to the ELO skill rating system. Next we will use the framework to analyse the robustness of the TrueSkill algorithm and discuss some of the findings. We have used simulated data to test the robustness of the TrueSkill algorithm. All of the data processing has been done in Python using our own code, built-in functions and Python packages. The code has primarily been used to make the simulations of matches and customise updating functions. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/CIG.2018.8490414 | 2018 IEEE Conference on Computational Intelligence and Games (CIG) |
Keywords | Field | DocType |
matchmaking,mathematical model,player satisfaction,online video games,skill rating,framework | User experience design,Random variable,Data processing,TrueSkill,Computer science,Rating system,Robustness (computer science),Artificial intelligence,Online video,Machine learning,Python (programming language) | Conference |
ISSN | ISBN | Citations |
2325-4270 | 978-1-5386-4360-0 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anders Harboell Christiansen | 1 | 0 | 0.34 |
| Bo Friis Nielsen | 2 | 45 | 10.84 |
| Emil Gensby | 3 | 0 | 0.34 |